Acceptability of a machine learning-powered clinical decision support system aiding serious illness conversation and its impact on clinical outcomes: A pilot study.
Authors: Teja Ganta, Stephanie Lehrman, Rachel Pappalardo, Irena Durkovic, Shira Lichtman, Brooke Tsembelis, Mark Liu, Robbie Freeman, Arash Kia, Prathamesh Parchure, Alla Keyzner, Madhu Mazumdar, Aarti Sonia Bhardwaj, Cardinale B. Smith
Journal: Journal of Clinical Oncology (2022)

6562 Background: Patients with advanced cancer that utilize end of life planning see benefits including better quality of life and medical care that is more consistent with their values. We developed a 30-day mortality predictive model using a machine learning algorithm and integrated it into a clinical decision support system (CDSS) that encourages clinicians to use the serious illness conversation (SIC) guide—a standardized questionnaire and conversational tool that facilitates end-of-life planning. The CDSS was piloted in the thoracic oncology clinic. We evaluated clinicians’ use of this system and its impact on patient outcomes. Methods: Between 4/14/21-1/15/22, information about patients identified by the model was sent to clinical teams via the electronic health record (EHR) to assess eligibility for a SIC. We reviewed the EHR for patients identified, SIC completion, and level of agreement by oncologists with the model. We evaluated the SIC guide responses using descriptive statistics and assessed differences in rates of hospice referral, hospital visits, and 30-day mortality by SIC completion status. Chi-squared test was used for testing association. Results: 94 patients were evaluated for SIC eligibility. Of these, oncologists agreed with 48 (51%) model predictions and SIC was completed for 28 (58%) of those patients. A median of 2.5 SIC eligibility assessments per week were completed, with a median time of 4 days from prediction to assessment. Likewise, a median of 1 SIC per week was completed, with a median time of 20 days from SIC eligibility assessment to conversation. Regarding the responses to the SIC guide, out of 28 patients, 75% have an appropriate understanding of their illness; 64% want to be fully informed of their medical information while 21% prefer information to be limited. Common patient goals were “being comfortable” (54%), “being at home” (29%) and “being independent” (25%). The most prevalent patient fears were “family concerns” (29%) or “physical suffering” (25%). The clinician who performs the SIC most often recommended an “additional conversation with physician” (39%), “conversation with family” (36%), or “referral to palliative care” (18%). SIC completion was associated with an increased rate of enrollment in hospice (33% vs 14%, P= 0.03) on univariate analysis. SIC was not associated with a difference in 30-day mortality or hospital visits. Multivariable analysis is ongoing. Conclusions: The machine-learning powered CDSS was adopted by the oncology care team within a reasonable timeframe. However, even if an oncologist used and agreed with the CDSS, the rate of eventual completion of SIC was not 100%. Additional barriers to SIC will be studied to optimize the CDSS. SIC completion may lead to increased enrollment in hospice and should continue to be studied as a standard component of comprehensive cancer care.